标签：text func bre 编写 default note 二维码 cat axis
从自动驾驶汽车检测路上的物体，到通过复杂的面部及身体语言识别发现可能的犯罪活动。多年来，研究人员一直在探索让机器通过视觉识别物体的可能性。
这一特殊领域被称为计算机视觉 (Computer Vision, CV)，在现代生活中有着广泛的应用。
目标检测 (ObjectDetection) 也是计算机视觉最酷的应用之一，这是不容置疑的事实。
现在的CV工具能够轻松地将目标检测应用于图片甚至是直播视频。本文将简单地展示如何用TensorFlow创建实时目标检测器。
TensorFlow版本在1.15.0或以上
执行pip install TensorFlow安装最新版本
一切就绪，现在开始吧！
第一步：从Github上下载或复制TensorFlow目标检测的代码到本地计算机
在终端运行如下命令：
git clonehttps://github.com/tensorflow/models.git第二步：安装依赖项
下一步是确定计算机上配备了运行目标检测器所需的库和组件。
下面列举了本项目所依赖的库。（大部分依赖都是TensorFlow自带的）
· Cython
· contextlib2
· pillow
· lxml
· matplotlib
若有遗漏的组件，在运行环境中执行pip install即可。
第三步：安装Protobuf编译器
谷歌的Protobuf，又称Protocol buffers，是一种语言无关、平台无关、可扩展的序列化结构数据的机制。Protobuf帮助程序员定义数据结构，轻松地在各种数据流中使用各种语言进行编写和读取结构数据。
Protobuf也是本项目的依赖之一。点击这里了解更多关于Protobufs的知识。接下来把Protobuf安装到计算机上。
打开终端或者打开命令提示符，将地址改为复制的代码仓库，在终端执行如下命令：
cd models/research
wget -Oprotobuf.zip https://github.com/protocolbuffers/protobuf/releases/download/v3.9.1/protoc-3.9.1-osx-x86_64.zip
unzipprotobuf.zip注意：请务必在models/research目录解压protobuf.zip文件。
来源：Pexels
第四步：编辑Protobuf编译器
从research/ directory目录中执行如下命令编辑Protobuf编译器：
./bin/protoc object_detection/protos/*.proto--python_out=.现在所有的依赖项都已经安装完毕，可以用Python实现目标检测了。
在下载的代码仓库中，将目录更改为：
models/research/object_detection这个目录下有一个叫object_detection_tutorial.ipynb的ipython notebook。该文件是演示目标检测算法的demo，在执行时会用到指定的模型：
ssd_mobilenet_v1_coco_2017_11_17这一测试会识别代码库中提供的两张测试图片。下面是测试结果之一：
要检测直播视频中的目标还需要一些微调。在同一文件夹中新建一个Jupyter notebook，按照下面的代码操作：
[1]:
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
# This isneeded since the notebook is stored in the object_detection folder.
sys.path.append("..")
from utils import ops as utils_ops
if StrictVersion(tf.__version__) < StrictVersion( 1.12.0 ):
raise ImportError( Please upgrade your TensorFlow installation to v1.12.*. )[2]:
# This isneeded to display the images.
get_ipython().run_line_magic( matplotlib , inline )[3]:
# Here arethe imports from the object detection module.
from utils import label_map_util
from utils import visualization_utils as vis_util[4]:
# Modelpreparation
# Anymodel exported using the `export_inference_graph.py` tool can be loaded heresimply by changing `PATH_TO_FROZEN_GRAPH` to point to a new .pb file.
# Bydefault we use an "SSD with Mobilenet" model here.
#See https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
#for alist of other models that can be run out-of-the-box with varying speeds andaccuracies.
# Whatmodel to download.
MODEL_NAME= ssd_mobilenet_v1_coco_2017_11_17
MODEL_FILE= MODEL_NAME + .tar.gz
DOWNLOAD_BASE= http://download.tensorflow.org/models/object_detection/
# Path tofrozen detection graph. This is the actual model that is used for the objectdetection.
PATH_TO_FROZEN_GRAPH= MODEL_NAME + /frozen_inference_graph.pb
# List ofthe strings that is used to add correct label for each box.
PATH_TO_LABELS= os.path.join( data , mscoco_label_map.pbtxt )[5]:
#DownloadModel
opener =urllib.request.URLopener()
opener.retrieve(DOWNLOAD_BASE+ MODEL_FILE, MODEL_FILE)
tar_file =tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
file_name= os.path.basename(file.name)
if frozen_inference_graph.pb in file_name:
tar_file.extract(file,os.getcwd())[6]:
# Load a(frozen) Tensorflow model into memory.
detection_graph= tf.Graph()
with detection_graph.as_default():
od_graph_def= tf.GraphDef()
withtf.gfile.GFile(PATH_TO_FROZEN_GRAPH, rb ) as fid:
serialized_graph= fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def,name= )[7]:
# Labelmaps map indices to category names, so that when our convolution networkpredicts `5`,
#we knowthat this corresponds to `airplane`. Here we use internal utilityfunctions,
#butanything that returns a dictionary mapping integers to appropriate stringlabels would be fine
category_index= label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,use_display_name=True)[8]:
defrun_inference_for_single_image(image, graph):
with graph.as_default():
with tf.Session() as sess:
# Get handles to input and output tensors
ops= tf.get_default_graph().get_operations()
all_tensor_names= {output.name for op in ops for output in op.outputs}
tensor_dict= {}
for key in [
num_detections , detection_boxes , detection_scores ,
detection_classes , detection_masks ]:
tensor_name= key + :0
if tensor_name in all_tensor_names:
tensor_dict[key]= tf.get_default_graph().get_tensor_by_name(tensor_name)
if detection_masks in tensor_dict:
# The following processing is only for single image
detection_boxes= tf.squeeze(tensor_dict[ detection_boxes ], [0])
detection_masks= tf.squeeze(tensor_dict[ detection_masks ], [0])
# Reframe is required to translate mask from boxcoordinates to image coordinates and fit the image size.
real_num_detection= tf.cast(tensor_dict[ num_detections ][0], tf.int32)
detection_boxes= tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
detection_masks= tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
detection_masks_reframed= utils_ops.reframe_box_masks_to_image_masks(
detection_masks,detection_boxes, image.shape[1],image.shape[2])
detection_masks_reframed= tf.cast(
tf.greater(detection_masks_reframed,0.5),tf.uint8)
# Follow the convention by adding back the batchdimension
tensor_dict[ detection_masks ] =tf.expand_dims(
detection_masks_reframed,0)
image_tensor= tf.get_default_graph().get_tensor_by_name( image_tensor:0 )
# Run inference
output_dict= sess.run(tensor_dict, feed_dict={image_tensor: image})
# all outputs are float32 numpy arrays, so convert typesas appropriate
output_dict[ num_detections ] =int(output_dict[ num_detections ][0])
output_dict[ detection_classes ] =output_dict[
detection_classes ][0].astype(np.int64)
output_dict[ detection_boxes ] =output_dict[ detection_boxes ][0]
output_dict[ detection_scores ] =output_dict[ detection_scores ][0]
if detection_masks in output_dict:
output_dict[ detection_masks ] =output_dict[ detection_masks ][0]
return output_dict[9]:
import cv2
cam =cv2.cv2.VideoCapture(0)
rolling = True
while (rolling):
ret,image_np = cam.read()
image_np_expanded= np.expand_dims(image_np, axis=0)
# Actual detection.
output_dict= run_inference_for_single_image(image_np_expanded, detection_graph)
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
output_dict[ detection_boxes ],
output_dict[ detection_classes ],
output_dict[ detection_scores ],
category_index,
instance_masks=output_dict.get( detection_masks ),
use_normalized_coordinates=True,
line_thickness=8)
cv2.imshow( image , cv2.resize(image_np,(1000,800)))
if cv2.waitKey(25) & 0xFF == ord( q ):
break
cv2.destroyAllWindows()
cam.release()在运行Jupyter notebook时，网络摄影系统会开启并检测所有原始模型训练过的物品类别。
来源：Pexels
感谢阅读本文，如果有什么建议，欢迎在留言区积极发言哟~
编译组：蔡思齐、孙梦琪
相关链接：
https://hackernoon.com/introductory-guide-to-real-time-object-detection-with-python-6jyb36t5
如需转载，请后台留言，遵守转载规范ACL2018论文集50篇解读
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标签：text func bre 编写 default note 二维码 cat axis
原文地址：https://blog.51cto.com/15057819/2565842
